How do SAP AI and HANA work together for ML?

 

Introduction

SAP AI Training helps professionals bridge AI with enterprise data.
In 2025, SAP sharpened integration between AI and HANA.
This article shows step-by-step how they combine for ML.

Table of Contents

·       Key Concepts of SAP AI and HANA

·       Key Differences and Roles

·       Key Examples in Practice

·       Benefits for Better Understanding

·       Step-by-Step Workflow

·       FAQs

1. Key Concepts of SAP AI and HANA

SAP AI focuses on models and services. HANA is the in-memory database and analytics engine. Together they handle data, training, and inference. In 2024–2025, SAP added tighter model lifecycle tools. Now, AI services can call HANA tables directly.

2. Key Differences and Roles

HANA stores and processes data fast. It runs SQL, calculation views, and streaming jobs. SAP AI runs model training and inference. It provides model governance and APIs. HANA acts as the data engine. SAP AI acts as the model engine.

3. Key Examples in Practice

Example 1: Predictive maintenance in manufacturing.

Step 1: HANA collects sensor data in real time.

Step 2: SAP AI trains a failure model.

Step 3: The model writes predictions back to HANA.

Step 4: SAP apps show alerts to users.

Example 2: Invoice automation for finance.

HANA stores invoice fields and history. SAP AI extracts text and predicts exceptions. Then HANA joins predictions with master data.
This improves automation and audit readiness.

4. Benefits for Better Understanding

Benefit 1: Speed. HANA reduces data latency.

Benefit 2: Scale. SAP AI handles many models.

Benefit 3: Governance. Model versions stay tracked.

Benefit 4: Integration. Apps call models via APIs.

Benefit 5: Cost control. Compute is used on demand.

5. Step-by-Step Workflow (Clear Results)

Step 1: Prepare data in HANA. Clean and model it.

Step 2: Export curated data to training pipelines.

Step 3: Train models in SAP AI environment. SAP AI Training Use frameworks.

Step 4: Validate model accuracy and fairness. Log metrics.

Step 5: Deploy model to production inference. Use containers.

Step 6: Connect inference outputs back to HANA. Store results.

Step 7: Monitor model drift with dashboards. Retrain as needed.

6. Toolchain and Latest 2025 Updates

In 2025, SAP introduced AI Launchpad updates. This enhances model monitoring and alerts. HANA Cloud now has improved data virtualization. It reduces movement of large datasets. Thus inference can run closer to data. SAP also improved integration with Joule assistant. This speeds up model deployment in business flows.

7. Where Learners Start

For practical learning, choose guided labs. Hands-on labs show HANA SQL and model APIs. Visualpath offers structured courses for working professionals. You can practice end-to-end ML scenarios step-by-step.

8. Practical Tips for Architects

Tip 1: Keep training data in HANA for lineage.

Tip 2: Use feature stores for reusable features.

Tip 3: Log model inputs and outputs in HANA.

Tip 4: Automate retraining on drift detection.

Tip 5: Secure data access with SAP BTP roles.

9. Key Examples (Short Cases)

Case A: Retail demand forecasting using HANA views.

Case B: Credit risk scoring with HANA master data.

Case C: HR attrition prediction linked to employee tables.

Each case pairs HANA for data with SAP AI for modeling.

FAQs

Q. Does SAP HANA use AI?

A. Yes. HANA supports in-database ML and calls external AI models.

Q. How do AI and ML work together?

A. AI uses models. ML builds models from data. Together they automate tasks.

Q. How to integrate AI into ERP system?

A. Expose model APIs and call them from ERP workflows. Store outputs in HANA.

Q. Is AI in ML or ML in AI?

A. ML is a subset of AI focused on learning from data. Visualpath explains both.

Conclusion

SAP AI and HANA together create a strong ML platform. They reduce data movement and speed decision making. You can learn this end-to-end flow with SAP AI Training. Also, a practical SAP AI Course Online helps build real skills. Start with data prep in HANA and end with model governance. Then monitor, retrain, and scale for business impact. Enroll, practice, and build production-ready ML pipelines today. You will gain in-demand skills for 2025 and beyond.

Visit our official website https://www.visualpath.in/sap-artificial-intelligence-training.html

or call us now https://wa.me/c/917032290546

to join the upcoming batch and fast-track your career in AI-driven enterprise innovation.